Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and predicting informal settlements using only street intersections data, regardless of the variation of urban form, number of floors, materials used for construction or street width. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and a machine learning approach, using Multinomial Logistic Regression (MNL) and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics that these regions tend to be filled in with smaller subdivided lots of housing relative to the formal areas within the local context, and the paucity of services and infrastructure within the boundary of these settlements that require relatively bigger lots. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context.

Trust in autonomy is essential for effective human-robot collaboration and user adoption of autonomous systems such as robot assistants. This paper introduces a computational model which integrates trust into robot decision-making. Specifically, we learn from data a partially observable Markov decision process (POMDP) with human trust as a latent variable. The trust-POMDP model provides a principled approach for the robot to (i) infer the trust of a human teammate through interaction, (ii) reason about the effect of its own actions on human trust, and (iii) choose actions that maximize team performance over the long term. We validated the model through human subject experiments on a table-clearing task in simulation (201 participants) and with a real robot (20 participants). In our studies, the robot builds human trust by manipulating low-risk objects first. Interestingly, the robot sometimes fails intentionally in order to modulate human trust and achieve the best team performance. These results show that the trust-POMDP calibrates trust to improve human-robot team performance over the long term. Further, they highlight that maximizing trust alone does not always lead to the best performance.

There are many examples of human decision making which cannot be modeled by classical probabilistic and logic models, on which the current AI systems are based. Hence the need for a modeling framework which can enable intelligent systems to detect and predict cognitive biases in human decisions to facilitate better human-agent interaction. We give a few examples of irrational behavior and use a generalized probabilistic model inspired by the mathematical framework of Quantum Theory to model and explain such behavior.

Designing a reliable natural language (NL) interface for querying tables has been a longtime goal of researchers in both the data management and natural language processing (NLP) communities. Such an interface receives as input an NL question, translates it into a formal query, executes the query and returns the results. Errors in the translation process are not uncommon, and users typically struggle to understand whether their query has been mapped correctly. We address this problem by explaining the obtained formal queries to non-expert users. Two methods for query explanations are presented: the first translates queries into NL, while the second method provides a graphic representation of the query cell-based provenance (in its execution on a given table). Our solution augments a state-of-the-art NL interface over web tables, enhancing it in both its training and deployment phase. Experiments, including a user study conducted on Amazon Mechanical Turk, show our solution to improve both the correctness and reliability of an NL interface.

This paper presents an artificial intelligence algorithm that can be used to derive formulas from various scientific disciplines called automatic derivation machine. First, the formula is abstractly expressed as a multiway tree model, and then each step of the formula derivation transformation is abstracted as a mapping of multiway trees. Derivation steps similar can be expressed as a reusable formula template by a multiway tree map. After that, the formula multiway tree is eigen-encoded to feature vectors construct the feature space of formulas, the Q-learning model using in this feature space can achieve the derivation by making training data from derivation process. Finally, an automatic formula derivation machine is made to choose the next derivation step based on the current state and object. We also make an example about the nuclear reactor physics problem to show how the automatic derivation machine works.

Bloomberg journalists have been breaking business news since 1990, but these days their reporting relies increasingly on data science. This change has thrust the head of data science, Gideon Mann, into a key role in the newsroom. The computer science graduate spent seven years as a staff research scientist at Google before he joined Bloomberg in 2014, but had little prior experience of finance and was initially surprised to see the influence that journalists have on markets. "Before I started at Bloomberg, I didn't understand the nature of how news moves markets," Mann told Computerworld UK from Bloomberg's new £1 billion European headquarters in the heart of the City of London. "Things happen in the real world and usually there's a journalist that's writing and talking about them and spreading the word, and that's how that information gets disseminated."

A couple years ago, we wrote about a robot called BALLU from Dennis Hong at UCLA--essentially a blimp with skinny little legs, BALLU made walking easier by taking gravity out of the equation. If your robot doesn't weigh anything, you don't have to worry about falling over, right? Inspired in part by BALLU, researchers from the University of Tokyo have developed a quadrotor with legs called Aerial-Biped. Designed primarily for entertainment, Aerial-Biped enables "a richer physical expression" by automatically generating walking gaits in sync with its quadrotor body. Until someone invents a robot that can moonwalk, you can model a gait that appears normal by simply making sure that the velocity of a foot is zero as long as it's in contact with the ground.

Most livestock and field crops rely heavily on the weather for their comfort, and providing water and energy. But China's more than 1.3 billion residents, a growing number of whom are becoming mid-income earners, are building up such an appetite that farmers are having to change the way they grow and sell food. In order to transform an ancient business that was largely run using intuition, the modern answer is technology. Artificial intelligence has come to the farmyard, helping to ensure the country's increasing numbers of pigs remain active and crop yields grow ever larger. This is the case for Wang Degen and his company Tequ Group, a major hog farm in Southwest China's Sichuan province.

Most of us already know that adopting new cloud applications can boost a business's productivity by enabling organizations to be more agile and ready to change course in our fast-moving and connected digital world. But the rapid adoption of cloud apps and services also brings with it profound security threats, including visibility and control challenges that aren't present in traditional on-premises environments. At the same time, the cloud - because of its interconnected, flexible and adaptable nature - can also provide new possibilities for addressing cloud security problems. By leveraging the power of the cloud with a data science and machine learning cloud-based solution, security and risk professionals can solve many of the traditional security challenges found in popular apps like Office 365, Google Drive, Salesforce and Box. In her session at 19th Cloud Expo, Deena Thomchick, Senior Director of Cloud Security at Symantec, detailed how cloud-based data science, machine learning, computational analysis and intelligent algorithms can work together to help to deliver truly intelligent and responsive security and compliance for the cloud.

In a race to dominate the artificial intelligence (AI) space, Microsoft is bringing the power of AI to users and organisations through'Microsoft Cognitive Services (MCS).' MCS is a collection of intelligent APIs (application programming interfaces) that allow systems to see, hear, speak, understand and interpret human needs using natural methods of communication. Products that analyse human emotions, AI technology that describes surroundings to visually-challenged and an Indian chatbot that interacts with users like a friend, were some of the innovations that Microsoft recently showcased at its research lab in Bengaluru. "We don't think about computers as competing with humans or replacing them," said Sriram Rajamani, distinguished scientist and managing director, Microsoft Research India Lab. "We think about them as amplifying human ability," he said.